Source:http://linkedlifedata.com/resource/pubmed/id/15628273
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
1
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| pubmed:dateCreated |
2005-1-4
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| pubmed:abstractText |
Abstract-Existing methods for incorporating subspace model constraints in shape tracking use only partial information from the measurements and model distribution. We propose a unified framework for robust shape tracking, optimally fusing heteroscedastic uncertainties or noise from measurement, system dynamics, and a subspace model. The resulting nonorthogonal subspace projection and fusion are natural extensions of the traditional model constraint using orthogonal projection. We present two motion measurement algorithms and introduce alternative solutions for measurement uncertainty estimation. We build shape models offline from training data and exploit information from the ground truth initialization online through a strong model adaptation. Our framework is applied for tracking in echocardiograms where the motion estimation errors are heteroscedastic in nature, each heart has a distinct shape, and the relative motions of epicardial and endocardial borders reveal crucial diagnostic features. The proposed method significantly outperforms the existing shape-space-constrained tracking algorithm. Due to the complete treatment of heteroscedastic uncertainties, the strong model adaptation, and the coupled tracking of double-contours, robust performance is observed even on the most challenging cases.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:month |
Jan
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| pubmed:issn |
0162-8828
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
27
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
115-29
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| pubmed:dateRevised |
2006-11-15
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| pubmed:meshHeading |
pubmed-meshheading:15628273-Algorithms,
pubmed-meshheading:15628273-Artificial Intelligence,
pubmed-meshheading:15628273-Cluster Analysis,
pubmed-meshheading:15628273-Computer Graphics,
pubmed-meshheading:15628273-Computer Simulation,
pubmed-meshheading:15628273-Echocardiography,
pubmed-meshheading:15628273-Image Enhancement,
pubmed-meshheading:15628273-Image Interpretation, Computer-Assisted,
pubmed-meshheading:15628273-Information Storage and Retrieval,
pubmed-meshheading:15628273-Models, Biological,
pubmed-meshheading:15628273-Models, Statistical,
pubmed-meshheading:15628273-Movement,
pubmed-meshheading:15628273-Numerical Analysis, Computer-Assisted,
pubmed-meshheading:15628273-Pattern Recognition, Automated,
pubmed-meshheading:15628273-Reproducibility of Results,
pubmed-meshheading:15628273-Sensitivity and Specificity,
pubmed-meshheading:15628273-Signal Processing, Computer-Assisted,
pubmed-meshheading:15628273-Subtraction Technique,
pubmed-meshheading:15628273-User-Computer Interface
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| pubmed:year |
2005
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| pubmed:articleTitle |
An information fusion framework for robust shape tracking.
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| pubmed:affiliation |
Integrated Data Systems Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540, USA. Xiang.Zhou@siemens.com
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| pubmed:publicationType |
Journal Article,
Comparative Study,
Evaluation Studies
|